• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.11 no.4
• /
• pp.582-591
• /
• 2000

In this paper, the Finite-Difference Time-Domain(FDTD) modeling method of the Korean human head is introduced to calculate electromagnetic energy absorption for the human head by mobile phones. After MRI scanning data is obtained, 2 dimensional(2D) segmentation is done from the 2D MRI image data by the semi-automatic method. Then, 3D dense segmentation data with $1mm\times1mm\times1mm$ is constructed from the 2D segmentation data. Using the 3D segmentation data, coarse FDTD models of human head that is tilted arbitrarily to model the condition of tilted usage of mobile phone.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2020.11a
• /
• pp.25-28
• /
• 2020

Automatic segmentation of brain tissues such as WM, GM, and CSF from brain MRI scans is helpful for the diagnosis of many neurological disorders. Accurate segmentation of these brain structures is a very challenging task due to low tissue contrast, bias filed, and partial volume effects. With the aim to improve brain MRI segmentation accuracy, we propose an end-to-end convolutional based U-SegNet architecture designed with multi-scale kernels, which includes cascaded dilated convolutions for the task of brain MRI segmentation. The multi-scale convolution kernels are designed to extract abundant semantic features and capture context information at different scales. Further, the cascaded dilated convolution scheme helps to alleviate the vanishing gradient problem in the proposed model. Experimental outcomes indicate that the proposed architecture is superior to the traditional deep-learning methods such as Segnet, U-net, and U-Segnet and achieves high performance with an average DSC of 93% and 86% of JI value for brain MRI segmentation.

• International Journal of Computer Science & Network Security
• /
• v.22 no.8
• /
• pp.343-351
• /
• 2022

A brain tumor forms when some tissue becomes old or damaged but does not die when it must, preventing new tissue from being born. Manually finding such masses in the brain by analyzing MRI images is challenging and time-consuming for experts. In this study, our main objective is to detect the brain's tumorous part, allowing rapid diagnosis to treat the primary disease instantly. With image processing techniques and deep learning prediction algorithms, our research makes a system capable of finding a tumor in MRI images of a brain automatically and accurately. Our tumor segmentation adopts the U-Net deep learning segmentation on the standard MICCAI BRATS 2018 dataset, which has MRI images with different modalities. The proposed approach was evaluated and achieved Dice Coefficients of 0.9795, 0.9855, 0.9793, and 0.9950 across several test datasets. These results show that the proposed system achieves excellent segmentation of tumors in MRIs using deep learning techniques such as the U-Net algorithm.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2009.01a
• /
• pp.55-58
• /
• 2009

Automatic segmentation of brain MRI data usually leaves some segmentation errors behind that are to be subsequently removed interactively, using computer graphics tools. This interactive removal is normally performed by operating on individual 2D slices. It is very tedious and still leaves some segmentation errors which are not visible on the slices. We have proposed to perform a novel 3D interactive correction of brain segmentation errors introduced by the fully automatic segmentation algorithms. We have developed the tool which is based on 3D semi-automatic propagation algorithm. The paper describes the implementation principles of the proposed tool and illustrates its application.

• Korean Journal of Radiology
• /
• v.20 no.1
• /
• pp.102-113
• /
• 2019

Objective: To assess the accuracy and potential bias of computed tomography (CT) ventricular volumetry using semiautomatic three-dimensional (3D) threshold-based segmentation in repaired tetralogy of Fallot, and to compare them to those of two-dimensional (2D) magnetic resonance imaging (MRI). Materials and Methods: This retrospective study evaluated 32 patients with repaired tetralogy of Fallot who had undergone both cardiac CT and MRI within 3 years. For ventricular volumetry, semiautomatic 3D threshold-based segmentation was used in CT, while a manual simplified contouring 2D method was used in MRI. The indexed ventricular volumes were compared between CT and MRI. The indexed ventricular stroke volumes were compared with the indexed arterial stroke volumes measured using phase-contrast MRI. The mean differences and degrees of agreement in the indexed ventricular and stroke volumes were evaluated using Bland-Altman analysis. Results: The indexed end-systolic (ES) volumes showed no significant difference between CT and MRI (p > 0.05), while the indexed end-diastolic (ED) volumes were significantly larger on CT than on MRI (93.6 ± 17.5 mL/m² vs. 87.3 ± 15.5 mL/m² for the left ventricle [p < 0.001] and 177.2 ± 39.5 mL/m² vs. 161.7 ± 33.1 mL/m² for the right ventricle [p < 0.001], respectively). The mean differences between CT and MRI were smaller for the indexed ES volumes (2.0-2.5 mL/m²) than for the indexed ED volumes (6.3-15.5 mL/m²). CT overestimated the stroke volumes by 14-16%. With phase-contrast MRI as a reference, CT (7.2-14.3 mL/m²) showed greater mean differences in the indexed stroke volumes than did MRI (0.8-3.3 mL/m²; p < 0.005). Conclusion: Compared to 2D MRI, CT ventricular volumetry using semiautomatic 3D threshold-based segmentation provides comparable ES volumes, but overestimates the ED and stroke volumes in patients with repaired tetralogy of Fallot.

• The Korean Journal of Applied Statistics
• /
• v.16 no.2
• /
• pp.305-319
• /
• 2003

This paper provides a non-Bayesian method based on the expanded EM algorithm for segmenting the magnetic resonance images degraded by bias field. For the images with the intensity as a pixel value, many segmentation methods often fail to segment it because of the bias field(with low frequency) as well as noise(with high frequency). Our contextual approach is appropriately designed by using normal mixture model incorporated with Markov random field for noise-corrective segmentation and by using the penalized likelihood to estimate bias field for efficient bias filed-correction.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39C no.6
• /
• pp.490-496
• /
• 2014

The thickness of knee joint cartilage causes most diseases of knee. Therefore, an articular cartilage segmentation of knee magnetic resonance imaging (MRI) is required to diagnose a knee diagnosis correctly. In particular, fully automatic segmentation method of knee joint cartilage enables an effective diagnosis of knee disease. In this paper, we analyze a well-known level-set based segmentation method in brain MRI, and apply that method to knee MRI with solving some problems from different image characteristics. The proposed method, a fully automatic segmentation in whole process, enables to process faster than previous semi-automatic segmentation methods. Also it can make a three-dimension visualization which provides a specialist with an assistance for the diagnosis of knee disease. In addition, the proposed method provides more accurate results than the existing methods of articular cartilage segmentation in knee MRI through experiments.

• Journal of Korea Multimedia Society
• /
• v.17 no.5
• /
• pp.566-572
• /
• 2014

The hippocampus has been known as one of the most important structure related to many neurological disorders, such as Alzheimer's disease. This paper presents the snake model to segment hippocampus from brain MRI. The snake model or active contour model is widely used in medical image processing fields, especially image segmentation they look onto nearby edge, localizing them accurately. We applied a snake model on brain MRI. Then we compared our results with an active shape approach. The results show that hippocampus was successfully segmented by the snake model.

• Journal of KIISE
• /
• v.42 no.10
• /
• pp.1222-1230
• /
• 2015

Segmentation of the anterior cruciate ligament (ACL) in knee MRI remains a challenging task due to its inhomogeneous signal intensity and low contrast with surrounding soft tissues. In this paper, we propose a multi-atlas-based segmentation of the ACL in knee MRI with locally-aligned probabilistic atlas (PA) in an iterative graph cuts framework. First, a novel PA generation method is proposed with global and local multi-atlas alignment by means of rigid registration. Second, with the generated PA, segmentation of the ACL is performed by maximum-aposteriori (MAP) estimation and then by graph cuts. Third, refinement of ACL segmentation is performed by improving shape prior through mask-based PA generation and iterative graph cuts. Experiments were performed with a Dice similarity coefficients of 75.0%, an average surface distance of 1.7 pixels, and a root mean squared distance of 2.7 pixels, which increased accuracy by 12.8%, 22.7%, and 22.9%, respectively, from the graph cuts with patient-specific shape constraints.

• International Journal of Computer Science & Network Security
• /
• v.24 no.2
• /
• pp.43-52
• /
• 2024

Medical imaginings assume a important part in the analysis of tumors and cerebrospinal fluid (CSF) leak. Magnetic resonance imaging (MRI) is an image segmentation technology, which shows an angular sectional perspective of the body which provides convenience to medical specialists to examine the patients. The images generated by MRI are detailed, which enable medical specialists to identify affected areas to help them diagnose disease. MRI imaging is usually a basic part of diagnostic and treatment. In this research, we propose new techniques using the 4D-MRI image segmentation process to detect the brain tumor in the skull. We identify the issues related to the quality of cerebrum disease images or CSF leakage (discover fluid inside the brain). The aim of this research is to construct a framework that can identify cancer-damaged areas to be isolated from non-tumor. We use 4D image light field segmentation, which is followed by MATLAB modeling techniques, and measure the size of brain-damaged cells deep inside CSF. Data is usually collected from the support vector machine (SVM) tool using MATLAB's included K-Nearest Neighbor (KNN) algorithm. We propose a 4D light field tool (LFT) modulation method that can be used for the light editing field application. Depending on the input of the user, an objective evaluation of each ray is evaluated using the KNN to maintain the 4D frequency (redundancy). These light fields' approaches can help increase the efficiency of device segmentation and light field composite pipeline editing, as they minimize boundary artefacts.